Program at a Glance
- Complete in 15 weeks
- Fully online with a blend of self-paced learning and required live virtual workshops
- Project-based learning with portfolio-ready deliverables
- Digital badge awarded upon completion
Analyze Data. Drive Informed Decisions.
Build the technical skills to take AI and machine learning solutions from development and evaluation to deployment and monitoring. The UC Davis AI and Machine Learning Professional Program combines foundational knowledge with hands-on practice to help you address real-world challenges. Whether you want to move into an AI or machine learning role or apply these technologies in your current field, you’ll learn to select, build and evaluate models—and communicate their performance, limitations and organizational value. Complete an applied industry project that demonstrates your abilities to employers.
Designed for scientists, engineers and analytical professionals, this program assumes experience working with data, familiarity with linear algebra and statistics, and the ability to write and debug basic code in a language such as Python.
What You'll Get
The AI and Machine Learning Professional Program combines flexible online coursework, instructor guidance and applied projects to help you develop practical technical skills.
- Instructor-led learning and required live workshops to reinforce concepts, support collaboration and guide your progress
- Coursera-integrated learning materials delivered through Canvas, UC Davis CPE’s learning management system
- An applied industry project facilitated through Riipen, connecting your learning with real business and organizational challenges
- A professional portfolio featuring your industry project, technical documentation and project demonstration, plus a digital badge recognizing successful program completion
What You'll Learn
Built around the Intensive Professional Learning Framework, this intensive program brings technical learning and applied project experience together in a structured online experience.
Throughout the program, you'll develop the skills to:
- Apply linear algebra, probability and optimization to understand model behavior
- Select machine learning, deep learning and large language model approaches for different problems and datasets
- Build, train and evaluate models in Python using tools such as scikit-learn, PyTorch and TensorFlow/Keras
- Develop generative AI applications using prompt engineering, retrieval-augmented generation and lightweight fine-tuning
- Write reproducible, version-controlled code
- Deploy models to the cloud and use foundational MLOps practices to monitor performance and detect drift
- Evaluate bias, explainability, privacy and governance considerations
- Communicate trade-offs involving accuracy, latency, cost and fairness
- Build a portfolio demonstrating your ability to develop and deploy a machine learning solution